SIMPL: Secure IoT Management Platform

The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However, despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL

Variations in achievement of evidence-based, high-impact quality indicators in general practice: an observational study

Background: There are widely recognised variations in the delivery and outcomes of healthcare but an incomplete understanding of their causes. There is a growing interest in using routinely collected ‘big data’ in the evaluation of healthcare. We developed a set of evidence-based ‘high impact’ quality indicators (QIs) for primary care and examined variations in achievement of these indicators using routinely collected data in the United Kingdom (UK). Methods: Cross-sectional analysis of routinely collected, electronic primary care data from a sample of general practices in West Yorkshire, UK (n = 89). The QIs covered aspects of care (including processes and intermediate clinical outcomes) in relation to diabetes, hypertension, atrial fibrillation, myocardial infarction, chronic kidney disease (CKD) and ‘risky’ prescribing combinations. Regression models explored the impact of practice and patient characteristics. Clustering within practice was accounted for by including a random intercept for practice. Results: Median practice achievement of the QIs ranged from 43.2% (diabetes control) to 72.2% (blood pressure control in CKD). Considerable between-practice variation existed for all indicators: the difference between the highest and lowest performing practices was 26.3 percentage points for risky prescribing and 100 percentage points for anticoagulation in atrial fibrillation. Odds ratios associated with the random effects for practices emphasised this; there was a greater than ten-fold difference in the likelihood of achieving the hypertension indicator between the lowest and highest performing practices. Patient characteristics, in particular age, gender and comorbidity, were consistently but modestly associated with indicator achievement. Statistically significant practice characteristics were identified less frequently in adjusted models. Conclusions: Despite various policy and improvement initiatives, there are enduring inappropriate variations in the delivery of evidence-based care. Much of this variation is not explained by routinely collected patient or practice variables, and is likely to be attributable to differences in clinical and organisational behaviour

Cytokinesis in yeast meiosis depends on the regulated removal of Ssp1p from the prospore membrane

Intracellular budding is a developmentally regulated type of cell division common to many fungi and protists. In Saccaromyces cerevisiae, intracellular budding requires the de novo assembly of membranes, the prospore membranes (PSMs) and occurs during spore formation in meiosis. Ssp1p is a sporulation-specific protein that has previously been shown to localize to secretory vesicles and to recruit the leading edge protein coat (LEP coat) proteins to the opening of the PSM. Here, we show that Ssp1p is a multidomain protein with distinct domains important for PI(4,5)P(2) binding, binding to secretory vesicles and inhibition of vesicle fusion, interaction with LEP coat components and that it is subject to sumoylation and degradation. We found non-essential roles for Ssp1p on the level of vesicle transport and an essential function of Ssp1p to regulate the opening of the PSM. Together, our results indicate that Ssp1p has a domain architecture that resembles to some extent the septin class of proteins, and that the regulated removal of Ssp1p from the PSM is the major step underlying cytokinesis in yeast sporulation

How can patients influence service improvement decision-making? A participatory research mixed methods study

BackgroundHealth policy promotes patient participation in decision-making about service organisation. In English general practice this happens through contractually required patient participation groups (PPGs). However, there are problems with the enactment of PPGs which have not been systematically addressed. AimTo observe how a co-designed theory-informed intervention can increase representational legitimacy and facilitate power-sharing to support PPGs to influence decision-making about general practice service improvement. Design and Setting Participatory action research to implement the intervention in two general practices in the North of England. The intervention combined two different participatory practices; partnership working involving externally facilitated meetings with PPG members and staff, and consultation with the wider patient population using a bespoke discrete choice experiment (DCE). MethodTo illustrate decision-making in PPGs qualitative data is presented from participant observation notes and photographed visual data generated through participatory methods. The DCE results are summarised to illustrate how wider population priorities contributed to overall decision-making. Observational data was thematically analysed using Normalisation Process Theory with support from a multi-stakeholder co-research group.ResultsIn both practices, patients influenced decision-making during PPG meetings and through the DCE, resulting in bespoke patient-centred action plans for service improvement. Power asymmetries were addressed through participatory methods, clarification of PPG roles in decision-making, and addressing representational legitimacy through wider survey consultation. ConclusionCombining participatory practices and facilitated participatory methods enabled patients to influence decision-making about general practice service improvement. The policy of mandatory PPGs needs updating to recognise the need to resource participation in a meaningful way

A Data Collection Tool to capture a Core Mental Health Dataset within Physical Health Clinical Trials

A Data Collection Tool for a Core Mental Health Dataset in Physical Health Research Studies Project Overview: The overarching aim is to create a seamless and widely acceptable mechanism to collect a common/core set of mental health data (the Core Mental Health Dataset - CMHDS) for all UK physical health study participants so that researchers can analyse the links between physical and mental health. CMHDS Data Collection Tool: The Core Mental Health DataSet (CMHDS) Open Source tool enables the collection of mental health data from participants in physical health studies. The CMHDS data collection tool was extensively co-designed with members of the public through a series of workshops. How participants engage with the tool: From a participant perspective, participants are provided with a URL which provides access to the CMHDS website. The website contains: A Participant Information Sheet A link to a short animation to explain the study in simple terms A consent form with mandatory and optional consents Clear information about how data collected will be used and for which purposes A bespoke mental health history questionnaire Validated questionnaires were included in the original CMHDS roll-out including: GHQ12, PHQ8 and GAD7. However, licenses are required to use some of the validated questionnaires and for this purpose they are not included in the source code here. These questionnaires (and others) can be easily added to a future deployment. Technical details of how to do this are included in the README.txt file. All questionnaires are optional to complete and the questionnaire can be exited at any time. There is an explanation of what each questionnaire entails that participants can read before answering them. The CMHDS website also contains a contact email so that participants can contact the CMHDS team if they have any questions and sources of support for people who have concerns about their mental health. Author Contributions Study Chief Investigator: Professor Kathryn Abel Co-investigators: Dr Pauline Whelan Professor Ann John Professor Paul Dark Professor Nawar Diar Bakerly PPI Lead Contributors Martin Rathfelder Philip Bell Researchers and Focus Group Facilitators Dr Kerry Gutridge Dr Jenni Jardine Auden Edwardes Operational Lead Charlie Stockton-Powdrell Lead Software UX designer - design of the website Simon Foster Software Engineers - development of the web app Henry Gorner Paul Smitton Acknowledgements: This study/project is funded by the NIHR Research for Patient Benefit, Award ID: NIHR201104. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Thanks to all of our patient and public contributors who attended workshops and helped co-design the CMHDS tool with us

Developing and evaluating packages to support implementation of quality indicators in general practice:the ASPIRE research programme, including two cluster RCTs

Background: Dissemination of clinical guidelines is necessary but seldom sufficient by itself to ensure the reliable uptake of evidence-based practice. There are further challenges in implementing multiple clinical guidelines and clinical practice recommendations in the pressurised environment of general practice. Objectives: We aimed to develop and evaluate an implementation package that could be adapted to support the uptake of a range of clinical guideline recommendations and be sustainably integrated within general practice systems and resources. Over five linked work packages, we developed ‘highimpact’ quality indicators to show where a measurable change in clinical practice can improve patient outcomes (work package 1), analysed adherence to selected indicators (work package 2), developed an adaptable implementation package (work package 3), evaluated the effects and cost-effectiveness of adapted implementation packages targeting four indicators (work package 4) and examined intervention fidelity and mechanisms of action (work package 5). Setting and participants: Health-care professionals and patients from general practices in West Yorkshire, UK. Design: We reviewed recommendations from existing National Institute for Health and Care Excellence clinical guidance and used a multistage consensus process, including 11 professionals and patients, to derive a set of ‘high-impact’ evidence-based indicators that could be measured using routinely collected data (work package 1). In 89 general practices that shared data, we found marked variations and scope for improvement in adherence to several indicators (work package 2). Interviews with 60 general practitioners, practice nurses and practice managers explored perceived determinants of adherence to selected indicators and suggested the feasibility of adapting an implementation package to target different indicators (work package 3).We worked with professional and patient panels to develop four adapted implementation packages. These targeted risky prescribing involving non-steroidal anti-inflammatory and antiplatelet drugs, type 2 diabetes control, blood pressure control and anticoagulation for atrial fibrillation. The implementation packages embedded behaviour change techniques within audit and feedback, educational outreach and (for risky prescribing) computerised prompts.We randomised 178 practices to implementation packages targeting either diabetes control or risky prescribing (trial 1), or blood pressure control or anticoagulation (trial 2), or to a further control (non-intervention) group, and undertook economic modelling (work package 4). In trials 1 and 2, practices randomised to the implementation package for one indicator acted as control practices for the other package, and vice versa. A parallel process evaluation included a further eight practices (work package 5). Main outcome measures: Trial primary end points at 11 months comprised achievement of all recommended levels of glycated haemoglobin, blood pressure and cholesterol; risky prescribing levels; achievement of recommended blood pressure; and anticoagulation prescribing. Results: We recruited 178 (73%) out of 243 eligible general practices.We randomised 80 practices to trial 1 (40 per arm) and 64 to trial 2 (32 per arm), with 34 non-intervention controls. The risky prescribing implementation package reduced risky prescribing (odds ratio 0.82, 97.5% confidence interval 0.67 to 0.99; p = 0.017) with an incremental cost-effectiveness ratio of £2337 per quality-adjusted life-year. The other three packages had no effect on primary end points. The process evaluation suggested that trial outcomes were influenced by losses in fidelity throughout intervention delivery and enactment, and by the nature of the targeted clinical and patient behaviours. Limitations: Our programme was conducted in one geographical area; however, practice and patient population characteristics are otherwise likely to be sufficiently diverse and typical to enhance generalisability to the UK.We used an ‘opt-out’ approach to recruit general practices to the randomised trials. Subsequently, our trial practices may have engaged with the implementation package less than if they had actively volunteered. However, this approach increases confidence in the wider applicability of trial findings as it replicates guideline implementation activities under standard conditions. Conclusions: This pragmatic, rigorous evaluation indicates the value of an implementation package targeting risky prescribing. In broad terms, an adapted ‘one-size-fits-all’ approach did not consistently work, with no improvement for other targeted indicators. Future work: There are challenges in designing ‘one-size-fits-all’ implementation strategies that are sufficiently robust to bring about change in the face of difficult clinical contexts and fidelity losses. We recommend maximising feasibility and ‘stress testing’ prior to rolling out interventions within a definitive evaluation. Our programme has led on to other work, adapting audit and feedback for other priorities and evaluating different ways of delivering feedback to improve patient care. Trial registration: Current Controlled Trials ISRCTN91989345.</p